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local SpatialBatchNormalization, parent = torch.class('cudnn.SpatialBatchNormalization', 'nn.SpatialBatchNormalization')
local ffi = require 'ffi'
local errcheck = cudnn.errcheck
SpatialBatchNormalization.__version = 2
function SpatialBatchNormalization:__init(nFeature, eps, momentum, affine)
parent.__init(self, nFeature, eps, momentum, affine)
self.mode = 'CUDNN_BATCHNORM_SPATIAL'
self.nFeature = nFeature
self.save_mean = torch.Tensor(nFeature)
self.save_std = torch.Tensor(nFeature)
end
function SpatialBatchNormalization:createIODescriptors(input)
assert(input:dim() == 4)
assert(torch.typename(self.weight) == 'torch.CudaTensor' and torch.typename(self.bias) == 'torch.CudaTensor',
'Only CUDA tensors are supported for cudnn.SpatialBatchNormalization!')
if not self.iDesc or not self.oDesc or
input:size(1) ~= self.iSize[1] or input:size(2) ~= self.iSize[2]
or input:size(3) ~= self.iSize[3] or input:size(4) ~= self.iSize[4] then
self.iSize = input:size()
self.output:resizeAs(input)
self.gradInput:resizeAs(input)
self.iDesc = cudnn.toDescriptor(input)
self.oDesc = cudnn.toDescriptor(self.output)
self.sDesc = cudnn.toDescriptor(self.bias:view(1, self.nFeature, 1, 1))
end
end
local one = torch.FloatTensor({1});
local zero = torch.FloatTensor({0});
local scaleTens = torch.FloatTensor(1);
function SpatialBatchNormalization:updateOutput(input)
self:createIODescriptors(input)
if self.train then
errcheck('cudnnBatchNormalizationForwardTraining',
cudnn.getHandle(), self.mode, one:data(), zero:data(),
self.iDesc[0], input:data(), self.oDesc[0], self.output:data(),
self.sDesc[0], self.weight:data(), self.bias:data(),
self.momentum, self.running_mean:data(), self.running_var:data(), self.eps, self.save_mean:data(), self.save_std:data());
else
errcheck('cudnnBatchNormalizationForwardInference',
cudnn.getHandle(), self.mode, one:data(), zero:data(),
self.iDesc[0], input:data(), self.oDesc[0], self.output:data(),
self.sDesc[0], self.weight:data(), self.bias:data(),
self.running_mean:data(), self.running_var:data(), self.eps);
end
return self.output
end
local function backward(self,input,gradOutput, scale)
assert(gradOutput:isContiguous())
self:createIODescriptors(input)
scale = scale or 1
scaleTens:fill(scale)
errcheck('cudnnBatchNormalizationBackward',
cudnn.getHandle(), self.mode, one:data(), zero:data(), scaleTens:data(), one:data(),
self.iDesc[0], input:data(), self.iDesc[0], gradOutput:data(), self.iDesc[0], self.gradInput:data(),
-- input is bottom, gradOutput is topDiff, self.gradInput is resultBottomDiff
self.sDesc[0], self.weight:data(), self.gradWeight:data(), self.gradBias:data(),
self.eps, self.save_mean:data(), self.save_std:data());
return self.gradInput
end
function SpatialBatchNormalization:updateGradInput(input, gradOutput, scale)
-- will in fact update gradWeight and gradBias too, accGradParameters call is empty
return backward(self, input,gradOutput, scale)
end
function SpatialBatchNormalization:backward(input, gradOutput, scale)
return backward(self, input,gradOutput, scale)
end
function SpatialBatchNormalization:accGradParameters(input, gradOutput, scale)
end
function SpatialBatchNormalization:write(f)
self.iDesc = nil
self.oDesc = nil
self.sDesc = nil
local var = {}
for k,v in pairs(self) do
var[k] = v
end
f:writeObject(var)
end
function SpatialBatchNormalization:read(file, version)
parent.read(self, file)
if version < 2 then
if self.running_std then
-- for models before https://github.com/soumith/cudnn.torch/pull/101
self.running_var = self.running_std:pow(-2):add(-self.eps)
self.running_std = nil
end
end
end
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